Interpolation based Fuzzy Automaton for Human-Robot Interaction

One way of handling Human-Robot Interaction (HRI) is based on the concept, that the robot acts like an animal companion to human. According to this paradigm the Robot should not be molded to mimic the human being, and form human-to-human like communication, but to follow the existing biological examples and form inter-species interaction. The 20.000 year old human-dog relationship is a good example for this paradigm of the HRI, as interaction of different species. One good reason of this approach in HRI is the lack of the “uncanny valley” effect i.e. increasing similarity of robots to humans will actually increase the chances that humans refuse interaction (will be frightened). In this paper, for ethologically inspired HRI model implementation, a fuzzy model structure built upon the framework of low computational demand Fuzzy Rule Interpolation (FRI) methods and fuzzy automaton is suggested. The application of FRI methods fits well the conceptually “spare rule-based” structure of the existing descriptive verbal ethological models. (In case of the descriptive verbal ethological models, the “completeness” of the rule-base is not required). The main benefit of the FRI method adaptation in ethological model implementation is the fact, that it has a simple rule-based knowledge representation format. Because of this, even after numerical optimization of the model, the rules are still “human readable”, and helps the formal validation of the model by the ethological experts. On the other side due to the FRI base, the model has still low computational demand and fits directly the requirements of the embedded implementations. For demonstrating the applicability of the proposed structure, some components of a human-dog interaction FRI model, which also suitable for HRI, will be briefly introduced in this paper.